code: def create_bag_none(
      breakpoints,
      n_instances,
      sfa_words,
      word_length,
      remove_repeat_words,
  ):
      """Create bag."""
      feature_count = np.uint32(breakpoints.shape[1] ** word_length)
      all_win_words = np.zeros(
          (n_instances, feature_count), dtype=np.uint32
      )
  
      for j in prange(sfa_words.shape[0]):
          # this mask is used to encode the repeated words
          if remove_repeat_words:
              masked = np.nonzero(sfa_words[j])
              all_win_words[j, :] = np.bincount(
                  sfa_words[j][masked], minlength=feature_count
              )
          else:
              all_win_words[j, :] = np.bincount(
                  sfa_words[j], minlength=feature_count
              )
  
      return all_win_words
  
file_path: sktime/sktime/transformations/panel/dictionary_based/_sfa_fast_numba.py
imports: import math
  from warnings import simplefilter
  import numpy
  from sktime.utils.numba.njit import njit
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 10 for calling a global function with example usage: create_bag_none(breakpoints, n_instances, sfa_words, word_length, remove_repeat_words) and returns: all_win_words